AimThe purpose of this study was to investigate the relationship between physical workload and injury risk in elite youth football players.MethodsThe workload data and injury incidence of 32 players were monitored throughout 2 seasons. Multiple regression was used to compare cumulative (1, 2, 3 and 4-weekly) loads and acute:chronic (A:C) workload ratios (acute workload divided by chronic workload) between injured and non-injured players for specific GPS and accelerometer-derived variables:total distance (TD), high-speed distance (HSD), accelerations (ACC) and total load. Workloads were classified into discrete ranges by z-scores and the relative risk was determined.ResultsA very high number of ACC (≥9254) over 3 weeks was associated with the highest significant overall (relative risk (RR)=3.84) and non-contact injury risk (RR=5.11). Non-contact injury risk was significantly increased when a high acute HSD was combined with low chronic HSD (RR=2.55), but not with high chronic HSD (RR=0.47). Contact injury risk was greatest when A:C TD and ACC ratios were very high (1.76 and 1.77, respectively) (RR=4.98).ConclusionsIn general, higher accumulated and acute workloads were associated with a greater injury risk. However, progressive increases in chronic workload may develop the players' physical tolerance to higher acute loads and resilience to injury risk.

Traditionally, golf biomechanics has focused upon achieving consistency in swing kinematics and kinetics, whilst variability was considered to be noise and dysfunctional. There has been a growing argument that variability is an intrinsic aspect of skilled motor performance and plays a functional role. Two types of variability are described: 'strategic shot selection' and 'movement variability'. In 'strategic shot selection', the outcome remains consistent, but the swing kinematics/kinetics (resulting in the desired ball flight) are free to vary; 'movement variability' is the changes in swing kinematics and kinetics from trial to trial when the golfer attempts to hit the same shot. These changes will emerge due to constraints of the golfer's body, the environment, and the task. Biomechanical research has focused upon aspects of technique such as elite versus non-elite kinematics, kinetics, kinematic sequencing, peak angular velocities of body segments, wrist function, ground reaction forces, and electromyography, mainly in the search for greater distance and clubhead velocity. To date very little is known about the impact of variability on this complex motor skill, and it has yet to be fully researched to determine where the trade-off between functional and detrimental variability lies when in pursuit of enhanced performance outcomes.

The most common bike fitting method to set the seat height is based on the knee angle when the pedal is in its lowest position, i.e. bottom dead centre (BDC). However, there is no consensus on what method should be used to measure the knee angle. Therefore, the first aim of this study was to compare three dynamic methods to each other and against a static method. The second aim was to test the intra-session reliability of the knee angle at BDC measured by dynamic methods. Eleven cyclists performed five 3-min cycling trials; three at different seat heights (25°, 30° and 35° knee angle at BDC according to static measure) and two at preferred seat height. Thirteen infrared cameras (3D), a high-speed camera (2D), and an electrogoniometer were used to measure the knee angle during pedalling, when the pedal was at the BDC. Compared to 3D kinematics, all other methods statistically significantly underestimated the knee angle (P = 0.00; η(2) = 0.73). All three dynamic methods have been found to be substantially different compared to the static measure (effect sizes between 0.4 and 0.6). All dynamic methods achieved good intra-session reliability. 2D kinematics is a valid tool for knee angle assessment during bike fitting. However, for higher precision, one should use correction factor by adding 2.2° to the measured value.

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Introduction30% of people over 65 years old and 50% of people over 80 years old have at least one fall per year (NICE, 2013). One avenue explored to attempt to reduce this fall risk is footwear.